Policy Gradient Methods often use which of the following to estimate the gradient of the expected reward with respect to the policy parameters?
- Monte Carlo estimation
- Finite difference
- Gradient ascent
- Random sampling
Policy Gradient Methods often use Monte Carlo estimation to estimate the gradient of the expected reward with respect to policy parameters. It involves sampling trajectories and averaging returns to estimate the gradient.
While t-SNE is excellent for visualization, it can sometimes produce misleading results due to which of its properties?
- Crowding Problem
- Curse of Dimensionality
- Convergence Issues
- Data Scaling
t-SNE can produce misleading results due to the "Curse of Dimensionality," which can lead to points appearing too clustered together in high-dimensional space, making it challenging to visualize and interpret.
In the context of autoencoders, what is the significance of the "bottleneck" layer?
- The bottleneck layer reduces model complexity
- The bottleneck layer enhances training speed
- The bottleneck layer compresses input data
- The bottleneck layer adds noise to data
The "bottleneck" layer in an autoencoder serves as the compression layer, reducing input data to a lower-dimensional representation. This compression is essential for capturing essential features in a compact representation, facilitating feature extraction and denoising.
The ________ gate in an LSTM controls which parts of the cell state should be updated.
- Update
- Forget
- Input
- Output
In an LSTM (Long Short-Term Memory), the update gate (also known as the input gate) regulates which parts of the cell state should be updated based on the current input and previous state.
Which algorithm can be used for both regression and classification tasks, and is particularly well-suited for dealing with large data sets and high-dimensional spaces?
- Gradient Boosting
- K-Means
- Naive Bayes
- Random Forest
Gradient Boosting is an algorithm that can be used for both regression and classification tasks. It's known for its robustness in handling large datasets and high-dimensional spaces, making it a versatile choice.
One of the common algorithms used to solve the multi-armed bandit problem is the ________ algorithm.
- UCB (Upper Confidence Bound)
- Q-Learning
- A* (A-Star)
- K-Means
The Upper Confidence Bound (UCB) algorithm is a common approach to solving the multi-armed bandit problem, providing a balance between exploration and exploitation.
When dealing with high-dimensional data, which of the two algorithms (k-NN or Naive Bayes) is likely to be more efficient in terms of computational time?
- Both Equally Efficient
- It depends on the dataset size
- Naive Bayes
- k-NN
Naive Bayes is generally more efficient in terms of computational time for high-dimensional data because it doesn't require distance calculations.
Why do traditional RNNs face difficulties in learning long-term dependencies?
- Vanishing Gradient Problem
- Overfitting
- Underfitting
- Activation Function Selection
Traditional RNNs face difficulties due to the "Vanishing Gradient Problem." During backpropagation, gradients can become extremely small, making it challenging to update weights for long sequences. This issue inhibits the model's ability to learn long-term dependencies effectively, a critical limitation in sequence data tasks.
Ridge and Lasso are techniques used for ________ to prevent overfitting.
- Data Preprocessing
- Feature Engineering
- Hyperparameter Tuning
- Regularization
Ridge and Lasso are both regularization techniques used to prevent overfitting in machine learning. Regularization adds penalty terms to the model's loss function to discourage excessive complexity and make the model generalize better.
Which algorithm is commonly used for density estimation in a dataset, especially when modeling clusters as ellipses?
- Gaussian Mixture Model
- k-Means
- Decision Tree
- Support Vector Machine
The Gaussian Mixture Model is frequently used for density estimation. It models data as a mixture of Gaussian distributions, allowing for flexible cluster shapes, including ellipses.